For decades, Cisco has built and operated some of the world’s most complex, mission-critical software systems. As generative AI matured from experimentation to real operational capability, Cisco leaned into what it knows best: scaling advanced technology inside demanding, real-world environments.
That mindset led Cisco to begin working closely with OpenAI around Codex, helping define what enterprise-grade AI for software engineering should look like in practice—and how Codex could be applied to real, large-scale engineering work inside complex production environments.
Rather than treat Codex as a standalone developer tool, Cisco began integrating it directly into production engineering workflows, exposing it to massive multi-repository systems, C/C++-heavy codebases, and the security, compliance, and governance requirements of a global enterprise.
In the process, Cisco helped shape Codex into something fundamentally different from a developer productivity tool: an AI engineering teammate capable of operating at enterprise scale.
"I’ve loved discovering new opportunities to integrate Codex into Cisco's enterprise software lifecycle workflows. Collaborating with the OpenAI team to get Codex enterprise production ready has been rewarding as well."
Cisco already runs a mature engineering organization with multiple AI initiatives in flight. What made Codex compelling wasn’t code completion or surface-level automation, but agency. Codex demonstrated the ability to:
- Understand and reason across large, interconnected repositories
- Work fluently in complex languages
- Execute real workflows through CLI-based, autonomous compile-test-fix loops
- Operate within existing review, security, and governance frameworks
By working directly with OpenAI, Cisco engineers were able to give feedback on how these capabilities behaved in real environments, shaping areas like workflow orchestration, security controls, and support for long-running engineering tasks—all of which are critical for enterprise use.
Once Codex was embedded into everyday engineering work, teams began applying it to some of their most challenging and time-consuming workflows:
Cross-repo build optimization: Codex analyzed build logs and dependency graphs across more than 15 interconnected repositories, identifying inefficiencies. The result: a ~20% reduction in build times and more than 1,500 engineering hours saved per month across global environments.
Defect remediation at scale (CodeWatch): Using Codex-CLI, Cisco automated defect repair with iterative, agentic execution on large-scale C/C++ codebases. What once took weeks of manual effort now completes in hours, delivering a 10-15× increase in defect resolution throughput and freeing engineers to focus on design and validation.
Framework migrations in days, not weeks: When Splunk teams needed to migrate multiple UIs from React 18 to 19, Codex handled the bulk of repetitive changes autonomously, compressing weeks of work into days and allowing engineers to concentrate on judgment-heavy decisions.
“The biggest gains came when we stopped thinking about Codex as a tool and started treating it as part of the team. We use Codex to generate and follow a plan document, allowing the reviewing team to more easily understand both the process and the code generated.”
Cisco provided continuous feedback from real production use that helped OpenAI accelerate Codex’s readiness for large enterprises—particularly in areas like compliance, long-running task management, and integration with existing development pipelines.
For Cisco, the collaboration established a repeatable model for adopting next-generation AI: deep technical partnership, real workloads, and leadership alignment from day one.
“Codex has become a meaningful part of how we think about AI-assisted development and operations going forward.”
In the months ahead, Cisco and OpenAI will continue to collaborate closely on Codex and beyond to advance their shared mission of AI-native engineering at enterprise scale.
